A cross‐industry survey was conducted to assess the landscape of preclinical quantitative systems pharmacology (QSP) modeling within pharmaceutical companies. This article presents the survey results, which provide insights on the current state of preclinical QSP modeling in addition to future opportunities. Our results call attention to the need for an aligned definition and consistent terminology around QSP, yet highlight the broad applicability and benefits preclinical QSP modeling is currently delivering.

Quantitative systems pharmacology (QSP) approaches have been increasingly applied in the pharmaceutical since the landmark white paper published in 2011 by a National Institutes of Health working group brought attention to the discipline. In this perspective, we discuss QSP in the context of other modeling approaches and highlight the impact of QSP across various stages of drug development and therapeutic areas. We discuss challenges to the field as well as future opportunities.

The complement system (CS) is an integral part of innate immunity and can be activated via three different pathways. The alternative pathway (AP) has a central role in the function of the CS. The AP of complement system is implicated in several human disease pathologies. In the absence of triggers, the AP exists in a time-invariant resting state (physiological steady state). It is capable of rapid, potent and transient activation response upon challenge with a trigger. Previous models of AP have focused on the activation response. In order to understand the molecular machinery necessary for AP activation and regulation of a physiological steady state, we built parsimonious AP models using experimentally supported kinetic parameters. The models further allowed us to test quantitative roles played by negative and positive regulators of the pathway in order to test hypotheses regarding their mechanisms of action, thus providing more insight into the complex regulation of AP.

Signal transduction pathways are characterized by complex biochemical reactions which involve a large number of proteins. The availability and quality of experimental data pose challenges for identifying the role of individual proteins in these pathways. To address this issue, this article formulates and solves an inverse problem to determine the dynamics of transcription factors from fluorescence intensity measurements of green fluorescent protein (GFP) reporter systems. In the presented approach, a model describing transcription and translation of GFP is discretized and concentrations of transcription factor are estimated at discrete time points. Unlike previous studies, this approach has no restrictions with regard to a particular shape of the profiles. However, the resulting inverse problem is ill‐conditioned and requires the use of regularization techniques. Two regularization methods—truncated singular value decomposition and Tikhonov regularization—are investigated in this work and the characteristics of the results obtained are discussed in detail. © 2012 American Institute of Chemical Engineers AIChE J, 2012

It is common that only a subset of the parameters of models can be accurately estimated. One approach for identifying a subset of parameters for estimation is to perform clustering of the parameters into groups based upon their sensitivity vectors. However, this has the drawback that uncertainty cannot be directly incorporated into the procedure as the sensitivity vectors are based upon the nominal values of the parameters. This article addresses this drawback by presenting a parameter set selection technique that can take uncertainty in the parameter space into account. This is achieved by defining sensitivity cones, where a sensitivity cone includes all sensitivity vectors of a parameter for different values, resulting from the uncertainty, in the parameter space. Parameter clustering can then be performed based upon the angles between the sensitivity cones, instead of the angle between sensitivity vectors. The presented technique is applied to two case studies. © 2013 American Institute of Chemical Engineers AIChE J, 60: 181–192, 2014